Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Neural Networks in Civil Engineering. I: Principles and Understanding
This map shows the geographic impact of Ian Flood's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ian Flood with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ian Flood more than expected).
This network shows the impact of papers produced by Ian Flood. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ian Flood. The network helps show where Ian Flood may publish in the future.
Co-authorship network of co-authors of Ian Flood
This figure shows the co-authorship network connecting the top 25 collaborators of Ian Flood.
A scholar is included among the top collaborators of Ian Flood based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Ian Flood. Ian Flood is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Issa, Raja R. A., et al.. (2016). Using particle swarm optimization to predict cost contingency on transportation construction projects. Journal of Information Technology in Construction. 21(30). 504–516.5 indexed citations
Flood, Ian, et al.. (2014). Development of a two-step neural network-based model to predict construction cost contingency. Journal of Information Technology in Construction. 19(24). 399–411.16 indexed citations
Flood, Ian, et al.. (2012). Computing in civil engineering : proceedings of the 2012 ASCE International Conference on Computing in Civil Engineering, June 17-20, 2012, Clearwater Beach, Florida. American Society of Civil Engineers eBooks.2 indexed citations
13.
Flood, Ian, et al.. (2006). Modified Linear Scheduling Method In Scheduling Multiple Utility Line Construction Project.1 indexed citations
Flood, Ian. (2004). Towards a vision for information technology in civil engineering : proceedings of the Fourth Joint International Symposium on Technology in Civil Engineering, November 15-16, 2003, Nashville, Tennessee. American Society of Civil Engineers eBooks.2 indexed citations
Flood, Ian & Nabil Kartam. (1998). Artificial Neural Networks for Civil Engineers: Advanced Features and Applications. American Society of Civil Engineers eBooks.45 indexed citations
18.
Kartam, Nabil, Ian Flood, & James H. Garrett. (1997). Artificial Neural Networks for Civil Engineers: Fundamentals and Applications. American Society of Civil Engineers eBooks.142 indexed citations
19.
Flood, Ian. (1997). Modeling Uncertainty in Cost Estimates: A Universal Extension of the Central Limit Theorem. Computing in Civil Engineering. 551–558.2 indexed citations
20.
Flood, Ian, et al.. (1994). The Application of Artificial Neural Networks to Civil Engineering. Computing in Civil Engineering. 668–675.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.